Semantic Segmentation of Sparsely Annotated 3D Point Clouds by Pseudo-Labelling

Katie Xu, Yasuhiro Yao, Kazuhiko Murasaki, Shingo Ando, Atsushi Sagata

Abstract: Manually labelling point clouds scenes for use as training data in machine learning applications is a time and labour intensive task. In this paper, we aim to reduce the effort associated with learning semantic segmentation tasks by introducing a semi -supervised method that operates on scenes with only a small number of labelled points. For this task, we advocate the use of pseudo-labelling in combination with PointNet, a neural network architecture for point cloud classification and segmentation. We also introduce a method for incorporating information derived from spatial relationships to aid in the pseudo-labelling process. This approach has practical advantages over current methods by working directly on point clouds and not being reliant on predefined features. Moreover, we demonstrate competitive performance on scenes from two publicly available datasets and provide studies on parameter sensitivity.